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Purpose This paper aims to discuss the estimation of PLS models with second-order formative constructs as existing research has mainly focused on second-order constructs with a reflective measurement. Design/methodology/approach Using a model grounded on Roger’s innovation diffusion theory applied to online travel shopping, an empirical application is used to assess and compare the different approaches used to estimate a formative second-order construct. The proposed model examines the innovations characteristics that have an impact on intentions to purchase travel online, using data from a convenience sample of 1,732 responses. Findings The findings show that all approaches produce similar results regarding the path coefficients, the predictive relevance of the model and the explained variance. The main differences between the approaches are related to the weights of the first-order constructs on the second-order construct and the significance of those weights. Several recommendations are made for researchers on which approach to use. Originality/value Since most research has focused on second-order constructs with a reflective measurement and there is limited research with formative second-order constructs, this paper provides a comparison of the different approaches typically used to estimate a formative second-order construct and present useful guidelines for researchers to decide the method to analyse a model with second-order constructs and how to assess formative second-order constructs.

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ABSTRACT. Over the past two decades, there has been an increasing focus on the development of Information and Communication Technologies (ICTs), as well as the impact that they have had on the tourism industry and on travelers' behaviors. However, research on what drives consumers to purchase travel online has typically been fragmented. In order to better understand consumers' behavior toward online travel purchasing, this article offers a review of articles that were published in leading tourism and hospitality journals, the ENTER proceedings, and several articles from other peer-reviewed journals, found on the main academic search databases. The antecedents of online travel shopping found are classified into three main categories: Consumer Characteristics, Perceived Channel Characteristics, and Website and Product Characteristics. Finally, this study identifies several gaps and provides some orientation for future research.

Purpose This study aims to apply the concept of brand love to a destination and investigate its antecedents and consequences. It also explores the moderating effects of time elapsed since the establishment of the destination brand love relationship on the outcomes of destination brand love. Design/methodology/approach A total of 5,511 valid responses were obtained from an online survey distributed among former international students from the Erasmus program of the European Union. Partial least squares structural equation modeling was conducted to assess the hypotheses. Findings Destination brand love was found to have a significant impact on electronic word of mouth (eWOM), WOM, WOM intensity, recommendation and revisit intention. Moderation analysis revealed that the amount of time elapsed since the establishment of the destination brand love relationship did not affect these outcomes. Moreover, destination image and the Erasmus experience had a positive effect on destination brand love. Practical implications Destination marketers should focus on enhancing the Erasmus experience and on improving destination image perception, as these factors help develop destination brand love. Marketers should also be aware that this relationship has long-lasting effects. Originality/value This study adds to the sparse literature on brand love in relation to a destination. This gives the first results for the importance of Erasmus students to the promotion of a host country. It also contributes to the question of how long the brand love relationship can last.

Purpose The purpose of this study is to examine Airbnb research using bibliometric methods. Using research performance analysis, this study highlights and provides an updated overview of Airbnb research by revealing patterns in journals, papers and most influential authors and countries. Furthermore, it graphically illustrates how research themes have evolved by mapping a co-word analysis and points out potential trends for future research. Design/methodology/approach The methodological design for this study involves three phases: the document source selection, the definition of the variables to be analyzed and the bibliometric analysis. A statistical multivariate analysis of all the documents’ characteristics was performed with R software. Furthermore, natural language processing techniques were used to analyze all the abstracts and keywords specified in the 129 selected documents. Findings Results show the genesis and evolution of publications on Airbnb research, scatter of journals and journals’ characteristics, author and productivity characteristics, geographical distribution of the research and content analysis using keywords. Research limitations/implications Despite Airbnb having a history of 10 years, research publications only started in 2015. Therefore, the bibliometric study includes papers from 2015 to 2019. One of the main limitations is that papers were selected in October of 2019, before the year was over. However, the latest academic publications (in press and earlycite) were included in the analysis. Originality/value This study analyzed bibliometric set of laws (Price’s, Lotka’s and Bradford’s) to better understand the patterns of the most relevant scientific production regarding Airbnb in tourism and hospitality journals. Using natural language processing techniques, this study analyzes all the abstracts and keywords specified in the selected documents. Results show the evolution of research topics in four periods: 2015-2016, 2017, 2018 and 2019.